All deep learning minimum variance distortionless response beamformer for speech separation and enhancement

ABSTRACT

A method, computer program, and computer system is provided for automated speech recognition. Audio data corresponding to one or more speakers is received. Covariance matrices of target speech and noise associated with the received audio data are estimated based on a gated recurrent unit-based network. A predicted target waveform corresponding to a target speaker from among the one or more speakers is generated by a minimum variance distortionless response function based on the estimated covariance matrices.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to speech recognition.

BACKGROUND

Deep learning based speech enhancement and speech separation methodshave attracted much research attention. A mask-based minimum variancedistortionless response (MVDR) beamformer could be used to reduce speechdistortion and be friendly to automated speech recognition. Acomplex-valued mask based multi-tap MVDR could be used to furtherimprove automated speech recognition performance in the mask-basedbeamforming framework.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forspeech recognition. According to one aspect, a method for speechrecognition is provided. The method may include receiving audio datacorresponding to one or more speakers. Covariance matrices of targetspeech and noise associated with the received audio data are estimatedbased on a gated recurrent unit-based network. A predicted targetwaveform corresponding to a target speaker from among the one or morespeakers is generated by a minimum variance distortionless responsefunction based on the estimated covariance matrices.

According to another aspect, a computer system for speech recognition isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving audio data corresponding to one or more speakers.Covariance matrices of target speech and noise associated with thereceived audio data are estimated based on a gated recurrent unit-basednetwork. A predicted target waveform corresponding to a target speakerfrom among the one or more speakers is generated by a minimum variancedistortionless response function based on the estimated covariancematrices.

According to yet another aspect, a computer readable medium for speechrecognition is provided. The computer readable medium may include one ormore computer-readable storage devices and program instructions storedon at least one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving audio data corresponding to one or more speakers.Covariance matrices of target speech and noise associated with thereceived audio data are estimated based on a gated recurrent unit-basednetwork. A predicted target waveform corresponding to a target speakerfrom among the one or more speakers is generated by a minimum variancedistortionless response function based on the estimated covariancematrices.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an exemplary speech recognition system, according to at leastone embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program that separates speech of target speakers, according to atleast one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to speech recognition. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, separate the speech of target speakers using an all-neuralnetwork approach. Therefore, some embodiments have the capacity toimprove the field of computing by allowing for improved speechenhancement, speech separation and dereverberation tasks by a computer.Moreover, the disclosed method, system, and computer-readable medium maybe used to improve the performance of automated speech recognition inareas such as hearing aids and communication.

As previously described, deep learning based speech enhancement andspeech separation methods have attracted much research attention. Amask-based minimum variance distortionless response (MVDR) beamformercould be used to reduce speech distortion and be friendly to automatedspeech recognition. A complex-valued mask based multi-tap MVDR could beused to further improve automated speech recognition performance in themask-based beamforming framework. However, the residual noise level isstill high, especially at low signal-to-noise ratios or overlappedspeech cases. Furthermore, the matrix inverse of the noise covariancematrix and PCA of the target speech covariance matrix, which areinvolved in the jointly trained MVDR and neural networks, are not stableand leading to less optimal results. Moreover, environmental noises andadverse room acoustics can greatly affect the quality of the speechsignal and therefore degrade the effectiveness of many speechcommunication systems (e.g., digital hearing-aid devices, and automaticspeech recognition (ASR) systems).

Speech enhancement and speech separation algorithms are thus disclosedto alleviate this problem. With the renaissance of neural networks,better objective performance can be achieved using deep learningmethods. However, it often results in greater amount of nonlineardistortion on the separated target speech, which harms the performanceof ASR systems. The minimum variance distortionless response (MVDR)filters aim to reduce the noise while keeping the target speechundistorted. More recently, MVDR systems with neural network (NN) basedtime-frequency (T-F) mask estimator can help greatly reduce the worderror rate (WER) of ASR systems with less amount of distortion, yetstill suffer from residual noise problems since chunk- orutterance-level beamforming weights are not optimal for noise reduction.Some frame-level MVDR weights estimation methods have been proposed, theauthors estimate the covariance matrix in a recursive way. Nevertheless,the calculated frame-wise weights are not stable when jointly trainedwith NNs. Previous studies have indicated that it is feasible for arecurrent neural network (RNN) to learn the matrix inversion efficientlyand that RNNs can better stabilize the process of matrix inversion andprincipal component analysis (PCA) when jointly trained with NNs.

It may be advantageous, therefore, to use RNNs to predict the matrixinverse of the noise covariance and the steering vector PCA of thetarget speech covariance matrix, rather than in a traditional mathematicway, for the mask-based MVDR beamforming framework. This could allow thewhole framework in an all jointly trained deep learning module.Different from the classical mask-based beamforming where only thechunk- or utterance-level weights could be calculated, the proposedADL-MVDR could adaptively obtain the frame-wise weights which isbeneficial to reduce the residual noise. As RNN is a recursive model,the covariance matrixes of the noise and the target speech could beautomatically updated in a recursive way without any manually setparameters. Additionally, a complex-valued filter may be used, ratherthan the commonly used per T-F bin mask, to calculate the covariancematrixes of the noise and the target speech. This may allow for a moreprecise estimation of covariance matrixes and stabilize the training ofRNN based matrix inverse and PCA. The jointly optimized complex-valuedfilter and the ADL-MVDR may be used in an end-to-end way.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

Referring now to FIG. 1, a functional block diagram of a networkedcomputer environment illustrating a speech recognition system 100(hereinafter “system”) for separating speech of target speakers using anall-neural network approach. It should be appreciated that FIG. 1provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 5 and 6. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for speech recognition isenabled to run a Speech Recognition Program 116 (hereinafter “program”)that may interact with a database 112. The Speech Recognition Programmethod is explained in more detail below with respect to FIG. 3. In oneembodiment, the computer 102 may operate as an input device including auser interface while the program 116 may run primarily on servercomputer 114. In an alternative embodiment, the program 116 may runprimarily on one or more computers 102 while the server computer 114 maybe used for processing and storage of data used by the program 116. Itshould be noted that the program 116 may be a standalone program or maybe integrated into a larger speech recognition program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2, an exemplary speech recognition system 200according to one or more embodiments is depicted. The speech recognitionsystem 200 may include, among other things, an audio input 202, a camera204, a complex ratio filter 206, gated recurrent unit (GRU) basednetworks (GRU-Nets) 208A and 208B, linear layers 210A and 210B, aframe-level weight module 212, and a speech separation module 214.

The direction of arrival (DOA) of the target speaker may be used forinforming the dilated convolutional neural networks (CNNs) to extractthe target speech from the multi-talker mixture. The audio input 202 mayreceive speaker-independent features (e.g., log-power spectra (LPS) andinteraural phase difference (IPD)) and speaker-dependent feature (e.g.,directional feature d(θ)). For example, the audio input 202 may be a15-element non-uniform linear microphone array that may be co-locatedwith the camera 204, which may be a wide-angle 180-degree camera. Thelocation of the target speaker's face in the whole view of the camera204 can provide a rough DOA estimation of the target speaker. A locationguided directional feature (DF) d(θ) may be used to extract the targetspeech from the specific DOA. The cosine similarity may be calculatedbetween the target steering vector v(θ) and IPDs. The estimated mask orfilter will help to calculate a covariance matrix Φ.

Consider a noisy speech mixture y=[y₁, y₂, . . . , y_(M)]^(T) recordedwith an M-size microphone array. s may represent a clean speech and nmay denote an interfering noise with M channels. Y(t,f)=S(t,f)+N(t,f),where (t,f) may indicate time and frequency indices of the acousticsignals in the T-F domain, and Y, S, N may denote the correspondingvariables in the T-F domain. The separated speech s_(MVDR)(t,f) can beobtained as

ŝ _(MVRD)(t,f)=h(f)^(H) Y(t,f)

where h(f)ϵ

^(M) may represent the MVDR weights at frequency index f and H standsfor the Hermitian operator. The goal of the MVDR beamformer may be tominimize the power of the noise while keeping the target speechundistorted, which can be formulated as:

$h_{MVDR} = {{{\underset{h}{\arg\mspace{14mu}\min\mspace{11mu} h}}^{H}\Phi_{NN}h\mspace{14mu}{s.t.\mspace{14mu} h^{H}}v} = 1}$

where Φ_(NN) stands for the covariance matrix of the noise power densityspectrum (PSD) and v(f)ϵ

^(M) denotes the steering vector of the target speech. Differentsolutions can be used to derive the MVDR beamforming weights. Onesolution may be based on the steering vector and can be derived byapplying principal component analysis (PCA) on the speech covariancematrix. The other solution can be derived based on the reference channelselection:

${{h(f)}^{v1} = \frac{{\Phi_{NN}^{- 1}(f)}{v(f)}}{{v(f)}^{H}{\Phi_{NN}^{- 1}(f)}{v(f)}}},{{h(f)} \in {\mathbb{C}}^{M}}$${h(f)}^{v2} = {\frac{{\Phi_{NN}^{- 1}(f)}{\Phi_{SS}(f)}}{{Trace}\left( {{\Phi_{NN}^{- 1}(f)}{\Phi_{SS}(f)}} \right)}u}$

where Φ_(SS) represents the covariance matrix of the speech PSD, and uϵ

^(M) is the one-hot vector selecting the reference microphone channel.Note that the matrix inversion and PCA may not be stable especially whenjointly trained with neural networks.

A complex ratio mask (denoted as cRM) can be used by the complex ratiofilter 206 to estimate the target speech accurately with less amount ofphase distortion, which benefits human listeners. In this case, theestimated speech ŝ_(cRM) and speech covariance matrix Φ_(SS) can becomputed as:

Ŝ_(cRM)(t, f) = cRM_(S)(t, f) * Y(t, f)${\Phi_{SS}(f)} = \frac{\sum\limits_{t = 1}^{T}\;{{{\hat{S}}_{cRM}\left( {t,f} \right)}{{\hat{S}}_{cRM}^{H}\left( {t,f} \right)}}}{\sum\limits_{t = 1}^{T}{{{cRM}_{S}^{H}\left( {t,f} \right)}{{cRM}_{S}\left( {t,f} \right)}}}$

where * denotes the complex multiplier and cRM_(S) represents theestimated cRM for speech target. The noise covariance matrix Φ_(NN) canbe obtained in a similar way. However, the covariance matrix Φ derivedhere is on the utterance level which is not optimal for each frame,resulting in high level of residual noise.

The GRU-Nets 208A,B may be used to replace the matrix inversion and PCAfor frame-level beamforming weights estimation. Using RNNs may utilizethe weighted information from all previous frames and may not need anyheuristic updating factors between consecutive frames as needed inrecursive approaches.

To better utilize the nearby T-F information and stabilize the estimatedstatistical variables (namely, Φ_(SS) and Φ_(NN)), the complex ratiofilter (cRF) 206 may be used to estimate the speech and noisecomponents. For each T-F bin, the cRF 206 may be applied to its K×Lnearby bins where K and L represent the number of nearby time andfrequency bins:

${{\hat{S}}_{cRF}\left( {t,f} \right)} = {\sum\limits_{t = 1}^{L}\;{\sum\limits_{f = 1}^{K}\;{{{cRF}\left( {t,f} \right)}*{Y\left( {t,f} \right)}}}}$${\Phi_{SS}(f)} = \frac{{{\hat{S}}_{cRF}\left( {t,f} \right)}{{\hat{S}}_{cRF}^{H}\left( {t,f} \right)}}{{{cRM}_{S}^{H}\left( {t,f} \right)}{{cRM}_{S}\left( {t,f} \right)}}$

where Ŝ_(cRF) indicates the estimated speech using the complex ratiofilter. The cRF 206 is equivalent to K×L number of cRMs that eachapplies to the corresponding shifted version (i.e., along time andfrequency axes) of the noisy spectrogram. The frame-level speechcovariance matrix is then computed with the center mask of the cRF(i.e., cRM_(S)(t,f) used for normalization. It may be appreciated thatthere may be no sum over the time dimension of Φ_(SS)(t,f) in order topreserve the frame-level temporal information. The frame-level noisecovariance matrix Φ_(NN)(t,f) can be obtained in a similar way.

The steering vector and the inverse of noise covariance matrix may beestimated with two GRU-Nets 208A,B. For h^(v2) solution, the speechcovariance matrix is also re-weighted using another GRU-Net. TheGRU-Nets 208A,B can better utilize temporal information from previousframes for statistical terms estimation than conventional frame-wiseapproaches that are based on heuristic updating factors. Additionally,replacing the matrix inversion with the GRU-Nets 208A,B may resolve aninstability issue during joint training with NNs. MVDR coefficients canbe obtained via the GRU-Nets as

{circumflex over (v)}(t,f)=GRU-Net_(v)(Φ_(SS)(t,f))

{circumflex over (Φ)}_(NN) ⁻¹(t,f)=GRU-Net_(NN)(Φ_(NN)(t,f))

{circumflex over (Φ)}_(SS)(t,f)=GRU-Net_(SS)(Φ_(SS)(t,f))

where the real and imaginary parts of the complex-valued covariancematrix Φ are concatenated together as input to the GRU-Nets 208A,B. Itmay be assumed that the explicitly calculated speech and noisecovariance matrices may be important for RNNs to learn the spatialfiltering, which may be different from the directly NN-learnedbeamforming weights. Leveraging on the temporal structure of RNNs, themodel recursively accumulates and updates the covariance matrix for eachframe. The output of each of the GRU-Nets 208A,B may be fed into linearlayers 210A,B to obtain the final real and imaginary parts of thecomplex-valued covariance matrices or steering vector. The frame-levelADL-MVDR weights may be computed by the frame-level weight module 212as:

${{h\left( {t,f} \right)}^{v1} = \frac{{\Phi_{NN}^{- 1}\left( {t,f} \right)}{\hat{v}\left( {t,f} \right)}}{{\hat{v}\left( {t,f} \right)}^{H}{{\hat{\Phi}}_{NN}^{- 1}\left( {t,f} \right)}{\hat{v}\left( {t,f} \right)}}},{{h\left( {t,f} \right)} \in {\mathbb{C}}^{M}}$${h\left( {t,f} \right)}^{v2} = {\frac{{{\hat{\Phi}}_{NN}^{- 1}\left( {t,f} \right)}{\Phi_{SS}\left( {t,f} \right)}}{{Trace}\left( {{{\hat{\Phi}}_{NN}^{- 1}\left( {t,f} \right)}{{\hat{\Phi}}_{SS}\left( {t,f} \right)}} \right)}u}$

where h(t,f) is frame-wise and different from the utterance-levelweights of conventional mask-based MVDR. Finally, the enhanced speech isobtained by the speech separation module 214 as:

Ŝ _(ADL-MVRD)(t,f)=h(t,f)^(H) Y(t,f)

Referring now to FIG. 3, an operational flowchart illustrating the stepsof a method 300 for speech recognition is depicted. In someimplementations, one or more process blocks of FIG. 3 may be performedby the computer 102 (FIG. 1) and the server computer 114 (FIG. 1). Insome implementations, one or more process blocks of FIG. 3 may beperformed by another device or a group of devices separate from orincluding the computer 102 and the server computer 114.

At 302, the method 300 includes receiving audio data corresponding toone or more speakers.

At 304, the method 300 includes estimating covariance matrices of targetspeech and noise associated with the received audio data based on agated recurrent unit-based network.

At 306, the method 300 includes generating a predicted target waveformcorresponding to a target speaker from among the one or more speakers bya minimum variance distortionless response function based on theestimated covariance matrices.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Speech Recognition Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 4, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Speech Recognition Program 116 (FIG. 1) canbe stored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theSpeech Recognition Program 116 (FIG. 1) on the server computer 114(FIG. 1) can be downloaded to the computer 102 (FIG. 1) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Speech RecognitionProgram 116 on the server computer 114 are loaded into the respectivehard drive 830. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Speech Recognition 96. Speech Recognition96 may separate the speech of target speakers using an all neuralnetwork approach.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of speech recognition, executable by aprocessor, comprising: receiving audio data corresponding to one or morespeakers; estimating covariance matrices of target speech and noiseassociated with the received audio data based on a gated recurrentunit-based network; and generating a predicted target waveformcorresponding to a target speaker from among the one or more speakers bya minimum variance distortionless response function based on theestimated covariance matrices.
 2. The method of claim 1, wherein thecovariance matrices correspond to a noise power density spectrum and aspeech power density spectrum.
 3. The method of claim 1, wherein thepredicted target waveform is generated using MVDR coefficientscorresponding to the covariance matrices.
 4. The method of claim 3,wherein the MVDR coefficients are calculated by the GRU-Net based onreal and imaginary parts of the covariance matrices being concatenatedby the GRU-Net.
 5. The method of claim 1, further comprising recursivelyaccumulating and updating the covariance matrices by the GRU-Net for oneor more frames.
 6. The method of claim 5, further comprising obtainingfinal real and imaginary components of covariance matrices using alinear layer.
 7. The method of claim 1, wherein the target speaker isidentified based on a direction of arrival corresponding to the receivedaudio data.
 8. A computer system for speech recognition, the computersystem comprising: one or more computer-readable non-transitory storagemedia configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: receiving code configured to cause the one ormore computer processors to receive audio data corresponding to one ormore speakers; estimating code configured to cause the one or morecomputer processors to estimate covariance matrices of target speech andnoise associated with the received audio data based on a gated recurrentunit-based network; and generating code configured to cause the one ormore computer processors to generate a predicted target waveformcorresponding to a target speaker from among the one or more speakers bya minimum variance distortionless response function based on theestimated covariance matrices.
 9. The computer system of claim 8,wherein the covariance matrices correspond to a noise power densityspectrum and a speech power density spectrum.
 10. The computer system ofclaim 8, wherein the predicted target waveform is generated using MVDRcoefficients corresponding to the covariance matrices.
 11. The computersystem of claim 10, wherein the MVDR coefficients are calculated by theGRU-Net based on real and imaginary parts of the covariance matricesbeing concatenated by the GRU-Net.
 12. The computer system of claim 8,further comprising accumulating code and updating code configured tocause the one or more computer processors to recursively accumulate andupdate the covariance matrices by the GRU-Net for one or more frames.13. The computer system of claim 12, further comprising obtaining codeconfigured to cause the one or more computer processors to obtain finalreal and imaginary components of the covariance matrices using a linearlayer.
 14. The computer system of claim 8, wherein the target speaker isidentified based on a direction of arrival corresponding to the receivedaudio data.
 15. A non-transitory computer readable medium having storedthereon a computer program for speech recognition, the computer programconfigured to cause one or more computer processors to: receive audiodata corresponding to one or more speakers; estimate covariance matricesof target speech and noise associated with the received audio data basedon a gated recurrent unit-based network; and generate a predicted targetwaveform corresponding to a target speaker from among the one or morespeakers by a minimum variance distortionless response function based onthe estimated covariance matrices.
 16. The computer readable medium ofclaim 15, wherein the covariance matrices correspond to a noise powerdensity spectrum and a speech power density spectrum.
 17. The computerreadable medium of claim 15, wherein the predicted target waveform isgenerated using MVDR coefficients corresponding to the covariancematrices.
 18. The computer readable medium of claim 17, wherein the MVDRcoefficients are calculated by the GRU-Net based on real and imaginaryparts of the covariance matrices being concatenated by the GRU-Net. 19.The computer readable medium of claim 15, wherein the computer programis further configured to cause one or more computer processors torecursively accumulate and update the covariance matrices by the GRU-Netfor one or more frames.
 20. The computer readable medium of claim 19,wherein the computer program is further configured to cause one or morecomputer processors to obtain final real and imaginary components of thecovariance matrices using a linear layer.